System and method for automatic control of exposure time in an imaging instrument

ABSTRACT

In an embodiment, a computer-implemented method of calibrating an imaging system in real-time, comprising: obtaining a first reading by a first sensor; establishing a dynamic link between the first reading and exposure time of a second sensor; using the dynamic link to control the exposure time of the second sensor; obtaining a second reading by the second sensor during the controlled exposure time; wherein the steps are performed by one or more computing devices.

BENEFIT CLAIM

This application claims the benefit of Provisional Application62/819,855, filed Mar. 18, 2019, the entire contents of which is herebyincorporated by reference as if fully set forth herein, under 35 U.S.C.§ 119(e).

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2015-2020 The Climate Corporation.

FIELD OF THE DISCLOSURE

One technical field of the present disclosure is computer-aidedprocessing of images captured using unmanned aircraft systems. Anothertechnical field of the present disclosure is automatically controllingexposure time in an imaging instrument. Another technical field of thepresent disclosure is processing and using, in real-time, data capturedby incident light sensors.

BACKGROUND

Imaging systems designers have produced numerous solutions thatreconcile limitation of detector sensitivity to light and variability inradiation signals coming from target surfaces. Most of these solutionsare focused on delivering maximum contrast of detector counts also knownas digital numbers (DN) in acquired imagery. Such approaches have beenproven effective in consumer camera products and have migrated intoimaging instruments on unmanned aircraft systems (UAS). Initial successwas demonstrated in applications that required only single reflectiveband imaging or multiple bands placed in close spectral proximity.However, UAS-carried imaging applications for agriculture use arechallenging the efficacy of contrast based approaches because of theneed to use light signals from distant portions of reflective spectrumwhile simultaneously preserving their relative proportions. Accuratedata collection is crucial for assessing field conditions, for example,to prevent losses in crop yield.

Thus, there is a need for a more refined and scientifically soundcapture of key spectral signals of crops.

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

SUMMARY

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 depicts an example embodiment of a timeline view for data entry.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry.

FIG. 7 illustrates an example UAS-carried imaging system, in accordancewith some embodiments.

FIG. 8 illustrates an example incident light sensor, in accordance withsome embodiments.

FIG. 9 illustrates an example method of controlling exposure time of anincident light sensor for a UAS mission, in accordance with someembodiments.

FIG. 10 illustrates an example target looking sensor, in accordance withsome embodiments.

FIG. 11 illustrates an example method for controlling exposure time inan imaging instrument, in accordance with some embodiments.

FIG. 12 illustrates an example method of calibrating a UAS-carriedimaging system, in accordance with some embodiments.

FIG. 13 illustrates an example workflow for a UAS-carried imaging systemduring a UAS mission, in accordance with some embodiments.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

1. GENERAL OVERVIEW

2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM

-   -   2.1. STRUCTURAL OVERVIEW    -   2.2. APPLICATION PROGRAM OVERVIEW    -   2.3. DATA INGEST TO THE COMPUTER SYSTEM    -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING    -   2.5. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW

3. EXAMPLE UAS-CARRIED IMAGING SYSTEM

-   -   3.1 CAPTURE OF INCIDENT LIGHT    -   3.2 LINKAGE TO EXPOSURE TIME OF TARGET LOOKING SENSOR    -   3.3 PROCEDURAL OVERVIEW

4. OTHER ASPECTS OF DISCLOSURE

1. General Overview

Control of exposure time in unmanned aircraft systems (UAS) carriedimaging instruments is critical for image quality and capability to seedifferences in light signals from targets. Variability in illuminationconditions and in optical properties of the targets create a challengefor capturing images as the capabilities of imaging instruments arelimited. According to various embodiments, computer-implemented methodsand systems are provided that employ a dedicated incident light sensorand readings therefrom to provide real-time processing and automaticcontrol of exposure time of a target looking sensor.

In one aspect, a computer-implemented method of calibrating an imagingsystem in real-time, comprising obtaining a first reading by a firstsensor, establishing a dynamic link between the first reading andexposure time of a second sensor, using the dynamic link to control theexposure time of the second sensor, obtaining a second reading by thesecond sensor during the controlled exposure time. The steps areperformed by one or more computing devices.

In another aspect, an imaging system comprises a first sensor, a secondsensor, and a system control board all communicatively coupled together.The first sensor is configured to obtain a first reading. The systemcontrol board is configured to establish a dynamic link between thefirst reading and exposure time of the second sensor and to use thedynamic link to control the exposure time of the second sensor. Thesecond sensor is configured to obtain a second reading during thecontrolled exposure time. Other aspects, features and embodiments willbecome apparent from the disclosure as a whole.

2 Example Agricultural Intelligence Computer System

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) chemical application data (for example, pesticide, herbicide,fungicide, other substance or mixture of substances intended for use asa plant regulator, defoliant, or desiccant, application date, amount,source, method), (g) irrigation data (for example, application date,amount, source, method), (h) weather data (for example, precipitation,rainfall rate, predicted rainfall, water runoff rate region,temperature, wind, forecast, pressure, visibility, clouds, heat index,dew point, humidity, snow depth, air quality, sunrise, sunset), (i)imagery data (for example, imagery and light spectrum information froman agricultural apparatus sensor, camera, computer, smartphone, tablet,unmanned aerial vehicle, planes or satellite), (j) scouting observations(photos, videos, free form notes, voice recordings, voicetranscriptions, weather conditions (temperature, precipitation (currentand over time), soil moisture, crop growth stage, wind velocity,relative humidity, dew point, black layer)), and (k) soil, seed, cropphenology, pest and disease reporting, and predictions sources anddatabases.

A data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of data thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 may have one or more remote sensors 112fixed thereon, which sensors are communicatively coupled either directlyor indirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, aerial vehiclesincluding unmanned aerial vehicles, and any other item of physicalmachinery or hardware, typically mobile machinery, and which may be usedin tasks associated with agriculture. In some embodiments, a single unitof apparatus 111 may comprise a plurality of sensors 112 that arecoupled locally in a network on the apparatus; controller area network(CAN) is example of such a network that can be installed in combines,harvesters, sprayers, and cultivators. Application controller 114 iscommunicatively coupled to agricultural intelligence computer system 130via the network(s) 109 and is programmed or configured to receive one ormore scripts that are used to control an operating parameter of anagricultural vehicle or implement from the agricultural intelligencecomputer system 130. For instance, a controller area network (CAN) businterface may be used to enable communications from the agriculturalintelligence computer system 130 to the agricultural apparatus 111, suchas how the CLIMATE FIELDVIEW DRIVE, available from The ClimateCorporation, San Francisco, Calif., is used. Sensor data may consist ofthe same type of information as field data 106. In some embodiments,remote sensors 112 may not be fixed to an agricultural apparatus 111 butmay be remotely located in the field and may communicate with network109.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a graphicalscreen display, such as a color display, that is mounted within anoperator's cab of the apparatus 111. Cab computer 115 may implement someor all of the operations and functions that are described further hereinfor the mobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1. The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, prescription maps, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, distributed databases, and any otherstructured collection of records or data that is stored in a computersystem. Examples of RDBMS's include, but are not limited to including,ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQLdatabases. However, any database may be used that enables the systemsand methods described herein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U. S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 5 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 5, a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline may includeNitrogen, Planting, Practices, and Soil. To add a nitrogen applicationevent, a user computer may provide input to select the nitrogen tab. Theuser computer may then select a location on the timeline for aparticular field in order to indicate an application of nitrogen on theselected field. In response to receiving a selection of a location onthe timeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 5, the top two timelines have the “Spring applied”program selected, which includes an application of 150 lbs N/ac in earlyApril. The data manager may provide an interface for editing a program.In an embodiment, when a particular program is edited, each field thathas selected the particular program is edited. For example, in FIG. 5,if the “Spring applied” program is edited to reduce the application ofnitrogen to 130 lbs N/ac, the top two fields may be updated with areduced application of nitrogen based on the edited program.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 5, the interface may update to indicatethat the “Spring applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Spring applied” program would not alter the April application ofnitrogen.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 6, a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 6. To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 6 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored or calculated output valuesthat can serve as the basis of computer-implemented recommendations,output data displays, or machine control, among other things. Persons ofskill in the field find it convenient to express models usingmathematical equations, but that form of expression does not confine themodels disclosed herein to abstract concepts; instead, each model hereinhas a practical application in a computer in the form of storedexecutable instructions and data that implement the model using thecomputer. The model may include a model of past events on the one ormore fields, a model of the current status of the one or more fields,and/or a model of predicted events on the one or more fields. Model andfield data may be stored in data structures in memory, rows in adatabase table, in flat files or spreadsheets, or other forms of storeddigital data.

In an embodiment, image retrieval instructions 136 comprises a set ofone or more pages of main memory, such as RAM, in the agriculturalintelligence computer system 130 into which executable instructions havebeen loaded and which when executed cause the agricultural intelligencecomputer system to perform the functions or operations that aredescribed herein with reference to those modules. For example, the imageretrieval instructions 136 may comprise a set of pages in RAM thatcontain instructions which when executed cause performing obtaining datafrom an unmanned aircraft system (UAS)-carried imaging system 700 asfurther described herein, for further analysis. The instructions may bein machine executable code in the instruction set of a CPU and may havebeen compiled based upon source code written in JAVA, C, C++,OBJECTIVE-C, or any other human-readable programming language orenvironment, alone or in combination with scripts in JAVASCRIPT, otherscripting languages and other programming source text. The term “pages”is intended to refer broadly to any region within main memory and thespecific terminology used in a system may vary depending on the memoryarchitecture or processor architecture. In another embodiment, the imageretrieval instructions 136 also may represent one or more files orprojects of source code that are digitally stored in a mass storagedevice such as non-volatile RAM or disk storage, in the agriculturalintelligence computer system 130 or a separate repository system, whichwhen compiled or interpreted cause generating executable instructionswhich when executed cause the agricultural intelligence computer systemto perform the functions or operations that are described herein withreference to those modules. In other words, the drawing figure mayrepresent the manner in which programmers or software developersorganize and arrange source code for later compilation into anexecutable, or interpretation into bytecode or the equivalent, forexecution by the agricultural intelligence computer system 130.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4. The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2 Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114 which include an irrigation sensor and/or irrigationcontroller. In response to receiving data indicating that applicationcontroller 114 released water onto the one or more fields, field managercomputing device 104 may send field data 106 to agriculturalintelligence computer system 130 indicating that water was released onthe one or more fields. Field data 106 identified in this disclosure maybe input and communicated using electronic digital data that iscommunicated between computing devices using parameterized URLs overHTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2, each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprises account,fields, data ingestion, sharing instructions 202 which are programmed toreceive, translate, and ingest field data from third party systems viamanual upload or APIs. Data types may include field boundaries, yieldmaps, as-planted maps, soil test results, as-applied maps, and/ormanagement zones, among others. Data formats may include shape files,native data formats of third parties, and/or farm management informationsystem (FMIS) exports, among others. Receiving data may occur via manualupload, e-mail with attachment, external APIs that push data to themobile application, or instructions that call APIs of external systemsto pull data into the mobile application. In one embodiment, mobilecomputer application 200 comprises a data inbox. In response toreceiving a selection of the data inbox, the mobile computer application200 may display a graphical user interface for manually uploading datafiles and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into management zones, such as the field map datalayers created as part of digital map book instructions 206. In oneembodiment, the management zones comprise soil zones along with a panelidentifying each soil zone and a soil name, texture, drainage for eachzone, or other field data. Mobile computer application 200 may alsodisplay tools for editing or creating such, such as graphical tools fordrawing management zones, such as soil zones, over a map of one or morefields. Planting procedures may be applied to all management zones ordifferent planting procedures may be applied to different subsets ofmanagement zones. When a script is created, mobile computer application200 may make the script available for download in a format readable byan application controller, such as an archived or compressed format.Additionally, and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops. This enables growers to maximize yield or return oninvestment through optimized nitrogen application during the season.Example programmed functions include displaying images such as SSURGOimages to enable drawing of fertilizer application zones and/or imagesgenerated from subfield soil data, such as data obtained from sensors,at a high spatial resolution (as fine as millimeters or smallerdepending on sensor proximity and resolution); upload of existinggrower-defined zones; providing a graph of plant nutrient availabilityand/or a map to enable tuning application(s) of nitrogen across multiplezones; output of scripts to drive machinery; tools for mass data entryand adjustment; and/or maps for data visualization, among others. “Massdata entry,” in this context, may mean entering data once and thenapplying the same data to multiple fields and/or zones that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields and/or zones of the same grower,but such mass data entry applies to the entry of any type of field datainto the mobile computer application 200. For example, nitrogeninstructions 210 may be programmed to accept definitions of nitrogenapplication and practices programs and to accept user input specifyingto apply those programs across multiple fields. “Nitrogen applicationprograms,” in this context, refers to stored, named sets of data thatassociates: a name, color code or other identifier, one or more dates ofapplication, types of material or product for each of the dates andamounts, method of application or incorporation such as injected orbroadcast, and/or amounts or rates of application for each of the dates,crop or hybrid that is the subject of the application, among others.“Nitrogen practices programs,” in this context, refer to stored, namedsets of data that associates: a practices name; a previous crop; atillage system; a date of primarily tillage; one or more previoustillage systems that were used; one or more indicators of applicationtype, such as manure, that were used. Nitrogen instructions 210 also maybe programmed to generate and cause displaying a nitrogen graph, whichindicates projections of plant use of the specified nitrogen and whethera surplus or shortfall is predicted; in some embodiments, differentcolor indicators may signal a magnitude of surplus or magnitude ofshortfall. In one embodiment, a nitrogen graph comprises a graphicaldisplay in a computer display device comprising a plurality of rows,each row associated with and identifying a field; data specifying whatcrop is planted in the field, the field size, the field location, and agraphic representation of the field perimeter; in each row, a timelineby month with graphic indicators specifying each nitrogen applicationand amount at points correlated to month names; and numeric and/orcolored indicators of surplus or shortfall, in which color indicatesmagnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium), application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, yield differential, hybrid, population, SSURGOzone, soil test properties, or elevation, among others. Programmedreports and analysis may include yield variability analysis, treatmenteffect estimation, benchmarking of yield and other metrics against othergrowers based on anonymized data collected from many growers, or datafor seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2, in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 232 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the field manager computing device104, agricultural apparatus 111, or sensors 112 in the field and ingest,manage, and provide transfer of location-based scouting observations tothe system 130 based on the location of the agricultural apparatus 111or sensors 112 in the field.

2.3 Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus; otherelectromagnetic radiation emitters and reflected electromagneticradiation detection apparatus. Such controllers may include guidance ormotor control apparatus, control surface controllers, cameracontrollers, or controllers programmed to turn on, operate, obtain datafrom, manage and configure any of the foregoing sensors. Examples aredisclosed in U.S. patent application Ser. No. 14/831,165 and the presentdisclosure assumes knowledge of that other patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

In an embodiment, sensors 112 and controllers 114 may comprise weatherdevices for monitoring weather conditions of fields. For example, theapparatus disclosed in U.S. Provisional Application No. 62/154,207,filed on Apr. 29, 2015, U.S. Provisional Application No. 62/175,160,filed on Jun. 12, 2015, U.S. Provisional Application No. 62/198,060,filed on Jul. 28, 2015, and U.S. Provisional Application No. 62/220,852,filed on Sep. 18, 2015, may be used, and the present disclosure assumesknowledge of those patent disclosures.

2.4 Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, fertilizer recommendations,fungicide recommendations, pesticide recommendations, harvestingrecommendations and other crop management recommendations. The agronomicfactors may also be used to estimate one or more crop related results,such as agronomic yield. The agronomic yield of a crop is an estimate ofquantity of the crop that is produced, or in some examples the revenueor profit obtained from the produced crop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge or sensorproviding weather data at the same or nearby location or an estimate ofnitrogen content with a soil sample measurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise, distorting effects, and confounding factorswithin the agronomic data including measured outliers that couldadversely affect received field data values. Embodiments of agronomicdata preprocessing may include, but are not limited to, removing datavalues commonly associated with outlier data values, specific measureddata points that are known to unnecessarily skew other data values, datasmoothing, aggregation, or sampling techniques used to remove or reduceadditive or multiplicative effects from noise, and other filtering ordata derivation techniques used to provide clear distinctions betweenpositive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared and/or validated usingone or more comparison techniques, such as, but not limited to, rootmean square error with leave-one-out cross validation (RMSECV), meanabsolute error, and mean percentage error. For example, RMSECV can crossvalidate agronomic models by comparing predicted agronomic propertyvalues created by the agronomic model against historical agronomicproperty values collected and analyzed. In an embodiment, the agronomicdataset evaluation logic is used as a feedback loop where agronomicdatasets that do not meet configured quality thresholds are used duringfuture data subset selection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5 Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infrared data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infrared signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3.0 Example UAS-Carried Imaging System

An unmanned aircraft system (UAS) is an aerial vehicle that can hoverabove ground, such as over crop fields. The UAS may include digitalimaging capabilities to provide farmers a richer picture of theirfields. In some embodiments, the UAS is equipped with an imaging systemto collect field-level data.

FIG. 7 illustrates an example UAS-carried imaging system, in accordancewith some embodiments. In an embodiment, a UAS-carried imaging system700 shown in FIG. 7 comprises an incident light sensor 705, a mainsystem control board 710 coupled to a target looking sensor 715 thatinteroperate under stored program control to provide exposure control toimprove the quality of data collected during a UAS mission. TheUAS-carried imaging system 700 may comprise a system storage device 720for storing data including images and a wireless networking interfacefor wirelessly communicating images or other data to a host computerand/or for receiving instructions, programs, or software updates.

In an embodiment, incident light sensor 705 is an upward looking sensorthat is positioned to measure the color spectrum of incident light froma light source, such as the Sun. In an embodiment, target looking sensor715 is a downward looking sensor that is positioned to detect lightreflected from target(s). Data captured by the incident light sensor 705and/or the target looking sensor 715 is processed in real-time or innear-time by a processor or micro-controller on the main system controlboard 710 to control the incident light sensor 705, the target lookingsensor 715, and/or other components of the UAS-carried imaging system700 which are not shown in FIG. 7 to avoid obscuring the focus of thedisclosure. The main system control board 710 may include a displayinterface and/or a communication interface. Parameter and control inputsmay be received via the communication interface.

The UAS-carried imaging system 700 of these embodiments providesadequate reading of incident light and adequate capture of lightreflected off a target. A target may be a specific object such as plant,a specific crop variety, an optical view, or an entire field orsubfield.

An adequate capture of light reflected off a target determines a valueof a final image product. However, by the nature of radiative transfer,the strength of light signal from the target is dependent on the amountof incident light, which in turn is determined by the position andstrength of the light source, such as the Sun. An adequate reading ofincident light may be set according to the Sun's position relative tothe target. The incident light reading is linked with the reflectedlight of the target to calibrate exposure time, or otherwise to controlexposure, of the target looking sensor 715.

3.1 Capture of Incident Light

The incident light sensor of the UAS-carried imaging system isconfigured to capture illumination conditions. At any time during properuse of the incident light sensor, the incident light sensor is expectedto give meaningful readings of energy of a given wavelength rangeintegrated over the hemisphere of the incident light sensor, which isreferred to as irradiance (E). The irradiance depends mostly on strengthand position of the light source. Knowledge of light source strength andposition will help set exposure time of the incident light sensor andensure meaningful readings, particularly when the light source is aswell characterized as the Sun.

FIG. 8 illustrates an example incident light sensor, in accordance withsome embodiments. The incident light sensor 705 of FIG. 7 may beconfigured as shown for the incident light sensor 800 of FIG. 8.Elements of the incident light sensor 800 are arranged linearly adjacentone another so that rays from a source of incident light within anintegration cone 825 pass through successive elements in a downwarddirection as oriented in FIG. 8. In an embodiment, the incident lightsensor 800 comprises a light detector 805, an exposure time controller810 linearly affixed above the light detector, a light filter 815linearly affixed above the exposure time controller, and a cosinecorrector/light diffuser 820 linearly affixed above the light filter815.

The light diffuser 820 is configured as a lens to capture light from alight source within the integration cone 825. When the light source isoutside the integration cone 825, the light diffuser 820 may not besensitive to light and/or the incident light sensor 800 may not givemeaningful readings. The captured light goes through the light filter815, which is configured to focus the light on a certain wavelength forthe exposure time controller 810 to control the time and the durationthe captured light will hit the light detector 805.

Some criteria for the incident light sensor 800 to provide meaningfulreadings include the light source being within the integration cone 825,no undersaturation of the light detector 805 when the light source is atlow elevation angles (high zenith angles), and no oversaturation of thelight detector 805 when the light source is a high elevation angles (lowzenith angles). To satisfy these criteria, acquisition time can berestricted to x hours after sunrise and y hours before sunset, andminimum solar irradiance (E_(Sun min)) for a given wavelength at thelowest elevation angle allowed by the light diffuser 820 and maximumsolar irradiance (E_(Sun max)) for a given wavelength at the highestelevation angle allowed by the light diffuser 820 can be obtained tocalibrate the incident light sensor 800 exposure time to capture allreadings between E_(Sun min) and E_(Sun max). A reading, by the incidentlight sensor 800, within the range of E_(Sun min) and E_(Sun max)transforms into observed solar irradiance (E_(Sun obs)).

In particular, the light detector 805 has a range of working conditionslimited by the amounts of light at which the light detector 805oversaturates and undersaturates. Knowing the rates of light per unittime from the maximum solar irradiance (E_(Sun max)), the exposure timecontroller 810 can limit the amount of energy hitting the light detector805 to avoid oversaturation. Additionally, knowing the minimum possiblerates of energy flow from the minimum solar irradiance (E_(Sun min)),the exposure time controller 810 can ensure sufficient amount of energyreaching the light detector 805 to avoid undersaturation.

In some embodiments, having the Sun as the light source, the maximumsolar irradiance (E_(Sun max)) for the time span of the UAS mission canbe pre-determined or dynamically calculated in real-time as the UAS isaware of its position and location at any moment in time duringoperation or a mission. In some embodiments, the minimum solarirradiance (E_(Sun min)) can be set statically at the sun radiation at alimiting zenith angle of the incident light sensor integration cone 825.A range of irradiances may thus be obtained. The range of irradiancescovers most of illumination conditions that can happen throughout theUAS mission, except for unusually dark cloud covers that can happen inconditions prohibited for UAS flights. To avoid sun positions outsidethe incident light sensor integration cone zenith angles, the missiontiming may be limited to a corresponding x hours after sunrise and yhours before sunset.

FIG. 9 illustrates an example method of controlling exposure time of anincident light sensor for a UAS mission, in accordance with someembodiments. The processes of each of FIG. 9, FIG. 11, FIG. 12, FIG. 13may be embodied in one or more sequences of executable programinstructions that the main system control board 710 executes, includingduring a UAS mission. The incident light sensor is part of theUAS-carried imaging system onboard of the UAS. The incident light sensorincludes basic elements such as a light detector, exposure timecontroller, light filter, and a cosine corrector/light diffuser.

Method 900 begins at a step 905, where restriction on acquisition timingis checked. In an embodiment, the acquisition timing is x hours aftersunrise and y hours before sunset. When the acquisition timing isoutside these bounds (for example, earlier than x hours after sunrise orlater than y hours before sunset), then a warning may be communicatedusing the display interface. An example warning may be that the incidentlight sensor may not be able to provide meaningful readings of energy.In an embodiment, the warning may also be transmitted to the fieldmanager computer device 104 of FIG. 1, the agricultural intelligencecomputer system 130 of FIG. 1, and/or other user computing devices andsystems.

At step 910, the solar irradiance (E_(Sun min)) for a given wavelengthat the lowest or minimum elevation angle allowed by the light diffuseris obtained. The minimum solar irradiance (E_(Sun min)) may be set atthe sun radiation at a limiting zenith angle of the incident lightsensor integration cone.

At step 915, the solar irradiance (E_(Sun max)) for a given wavelengthat the highest or maximum elevation angle of the UAS mission isobtained. The maximum solar irradiance (E_(Sun max)) for the time spanof the UAS mission may be set at the maximum elevation angle of the UASmission.

In some embodiments, the maximum solar irradiance (E_(Sun max)), theminimum solar irradiance (E_(Sun min)), or both may be obtained frompublicly available sources of information on sun position and onseasonal atmospheric conditions. Example publicly available sources arenonprofit organizations and governmental departments/agencies includinglibraries. Step 910 and step 915 may be performed in parallel, or step915 may be performed before step 910.

At step 920, exposure time of the incident light sensor is calibrated togive meaningful readings. In some embodiments, the incident light sensoruses the E_(Sun max) to set the exposure time. In some embodiments, theexposure time controller is calibrated to capture all readings betweenE_(Sun max) and E_(Sun min). A meaningful reading transforms into anobserved solar irradiance (E_(Sun obs)), which indicates the amount oflight of the Sun at a particular point in time, position, and location.

The method 900 optimizes exposure settings in the incident light sensorof the UAS-carried imaging system.

3.2 Linkage to Exposure Time of Target Looking Sensor

Since most of the light on Earth's surface is reflected sunlight, it canbe assumed that a function of all reflected sunlight (E_(Sun)) is afunction of a reading of the incident light sensor(Reading_(Incident light sensor)), which can be used to calibrateexposure time of a target looking sensor(Exposure_(Target looking sensor)). These relationships are illustratedby the following:

Exposure_(Target looking sensor) =f(Reading_(Incident light sensor))=f(E_(Sun))  [Eq. 1]

FIG. 10 illustrates an example target looking sensor, in accordance withsome embodiments. The target looking sensor 715 of FIG. 7 may beconfigured as shown for the target looking sensor 1000 of FIG. 10.Elements of the target looking sensor 1000 are arranged linearlyadjacent one another so that light reflected from a target(s) within afield of view 1025 passes through successive elements in an upwarddirection as oriented in FIG. 10. In an embodiment, the target lookingsensor 1000 comprises as an array of light detectors 1005, an exposuretime controller 1010 linearly affixed below the array of light detectors1005, a light filter 1015 linearly affixed below the exposure timecontroller 1010, and a lens 1020 linearly affixed below the light filter1015.

Light reflected from a target is captured by the lens 1020 within afield of view 1025 of the lens 1020. A target may be a specific objectsuch as a plant, a specific crop variety, an optical view, or an entirefield. The captured light goes through the light filter 1015, which isconfigured to focus the light on a certain wavelength for the exposuretime controller 1010 to control the time and the duration the capturedlight will hit the array of light detectors 1005, which is used to forman image. Each detector in the array of light detectors 1005 has its ownfield of view 1030 that is within the target looking sensor's field ofview 1025.

Similar to the incident light sensor 800, the target looking sensor 1000has a range of working conditions limited by the amounts of light atwhich the light detectors 1005 in the target looking sensor 1000oversaturate and undersaturate. These limits may be characterized byradiances, which are energies going through a field of view 1030 of eachlight detector 1005 per unit time. With an assumption of uniform lightdistribution in the lower hemisphere, corresponding irradiance valuescan be determined by integration such that each light detector 1005reading includes a pair of radiance value and irradiance value.

The relationship between a reading by the incident light sensor 800 andexposure of the target looking sensor 1000 may be established bycharacterizing the light detectors 1005 with a nominal exposure time anddetermining the maximum irradiance value of the light detectors 1005(E_(Detector max)), which indicates the maximum sensitivity of the lightdetectors 1005 or the maximum light the light detectors 1005 can manageunder the nominal exposure time.

E_(Detector max) may be determined empirically. Alternatively,E_(Detector max) may be determined from a corresponding maximum radiancevalue of the light detectors 1005 (L_(Detector max)), assuming anisotropic light distribution. In some embodiments, L_(Detector max) isdetermined by using an observed solar irradiance (E_(Sun obs)) andcomparing radiance values of the light detectors at a particular pointin time, position, and location associated with E_(Sun obs).

The relationship between E_(Detector max) and L_(Detector max) isillustrated by the following equation:

$\begin{matrix}{E_{{Detector}\mspace{14mu} \max} = \frac{\left( {L_{{Detector}\mspace{14mu} \max}*4\pi} \right)}{{STR}_{Detector}}} & \left\lbrack {{Eq}.\mspace{14mu} 2} \right\rbrack\end{matrix}$

STR_(Detector) is the field of view of the detector at nadir insteradians, and L_(Detector max) is the maximum radiance value of thelight detectors 1005 at saturation.

The relative scale between the observed solar irradiance (E_(Sun obs))and the maximum target detector irradiance (E_(Detector max)) can thenbe obtained. This is useful because, within optical wavelengths of thespectrum at the Earth surface, almost all of the entire light signalcoming to the target looking sensor 1000 is reflected from targetsurfaces, which depends on incident light from the Sun (E_(Sun obs)).

The proportion between E_(Sun obs) and E_(Detector max) can be used toscale the nominal exposure time of the array light detectors 1005 in thetarget looking sensor 1000. In some embodiments, the nominal exposuretime (Detector Exposure Time_(Normal)) is determined during sensordesign when exposing the array of light detectors 1005 to radiancesbetween levels of undersaturation and oversaturation.

The calculation of exposure time of the array of light detectors 1005can be performed by solving the following proportion set:

$\begin{matrix}{\frac{E_{{Sun}\mspace{14mu} {obs}}}{E_{{Detector}\mspace{14mu} \max}} = \frac{{Detector}\mspace{14mu} {Exposure}\mspace{14mu} {Time}_{Current}}{{Detector}\mspace{14mu} {Exposure}\mspace{14mu} {Time}_{Nominal}}} & \left\lbrack {{Eq}.\mspace{14mu} 3} \right\rbrack\end{matrix}$

Solving Equation 3 for Detector Exposure Time_(Current), avoidsunderstaturation and overstaturation of the array of light detectors1005.

In some embodiments, Equation 3 allows the array of light detectors 1005of the target looking sensor 1000 to make meaningful capture of signalsfrom surfaces ranging from 0 to 1 in their reflectivity properties. Thisrange cannot be observed by traditional imaging systems purposed forin-season observation of crops or other vegetation types.

Knowledge of target reflectivity properties further optimizes exposuretime of the target looking sensor 1000 of the UAS-carried imagingsystem. The ranges of reflectivity properties for different wavelengthbands may be obtained from publicly available and/or proprietaryradiative transfer models relating to optical properties of targets andtheir changes in time. The maximum reflectivity of a target(ρ_(Max target)) can be applied to Equation 3 to narrow focus ofdetector sensitivity to specific target signals, as illustrated by thefollowing equation:

$\begin{matrix}{\frac{\rho_{{Max}\mspace{14mu} {target}}*E_{{Sun}\mspace{14mu} {obs}}}{E_{{Detector}\mspace{14mu} \max}} = \frac{{Detector}\mspace{14mu} {Exposure}\mspace{14mu} {Time}_{Current}}{{Detector}\mspace{14mu} {Exposure}\mspace{14mu} {Time}_{Nominal}}} & \left\lbrack {{Eq}.\mspace{14mu} 4} \right\rbrack\end{matrix}$

Solving Equation 4 for Detector Exposure Time_(current) completesoptimization of exposure settings in the UAS-carried imaging system.

Further steps may be taken to optimize gains of the target lookingsensor 1000 by adjusting for the minimum target reflectivity of a target(ρ_(Min target)). This would enhance radiometric resolution, which isout the scope of this disclosure.

FIG. 11 illustrates an example method of controlling exposure time of atarget looking sensor for a UAS mission, in accordance with someembodiments. The target looking sensor is part of the UAS-carriedimaging system onboard of the UAS. The target looking sensor includesbasic elements such as an array of light detectors, exposure timecontroller, light filter, and a lens.

Method 1100 begins at a step 1105, where an observed solar irradiance(E_(Sun obs)) is obtained. E_(Sun obs) may be obtained such as from themethod 900. Alternatively, E_(Sun obs) may be obtained by usingadditional device(s), such as a pyranometer, and/or calibration step(s),such as spectral adjustment, purposed to obtain E_(Sun obs) instantly.

At step 1110, the maximum irradiance value of the light detectors(E_(Detector max)) is determined. In some embodiments, theE_(Detector max) is determined empirically. In other embodiments,E_(Detector max) is determined from a corresponding maximum radiancevalue of the array of light detectors (L_(Detector max)), assuming anisotropic light distribution. The relationship between E_(Detector max)and L_(Detector max) is illustrated by Equation 2.

At step 1115, exposure time of the target looking sensor is calibratedby using the proportion between the observed solar irradiance(E_(Sun obs)) from step 1105 and the maximum target detector irradiance(E_(Detector max)) from step 1110 to scale a nominal exposure time(Detector Exposure Time_(Nominal)) of the array light detectors in thetarget looking sensor. In some embodiments, the nominal exposure time isdetermined during sensor design when exposing the array of lightdetectors to radiances between levels of undersaturation andoversaturation.

In some embodiments, the maximum reflectivity of a specific target(ρ_(Max target)) can be obtained and applied, at step 1115, as a factorto narrow the range of sensitivity for better sensitivity to signals ofthe target.

The method 1100 optimizes exposure settings in the target looking sensorof the UAS-carried imaging system.

3.3 Procedural Overview

FIG. 12 illustrates an example method of calibrating a UAS-carriedimaging system, in accordance with some embodiments. In someembodiments, method 1200 of FIG. 12 relates to an imaging systemcharacterization procedure performed in a laboratory setting.Characterizations and/or calibrations are needed for proper functioningof different components of the UAS-carried imaging system during fieldmissions.

Method 1200 begins at step 1205, where critical energy levels for alight detector of an incident light sensor are characterized at variousexposure times. Step 1205 may be performed in a laboratory setting. Thesolar irradiance (E_(Sun min)) for a given wavelength at the lowestelevation angle allowed by a light diffuser of the incident light sensorand the solar irradiance (E_(Sun max)) for a given wavelength at themaximum elevation angle of an acquisition campaign (for example, a UASmission) are obtained. E_(Sun min) and/or E_(Sun max) may be obtainedfrom publicly available sources of information on sun position and onseasonal atmospheric conditions.

At step 1210, exposure time controller of the incident light sensor iscalibrated to capture all readings between E_(Sun max) and E_(Sun min).A broad range of conditions may be simulated in order to capturevariabilities of solar irradiance values of E_(Sun max) and E_(Sun min).A reading is converted to observed solar irradiance (E_(Sun obs)).

At step 1215, critical energy levels for an array of light detectors ofa target looking sensor are characterized. The maximum radiance andirradiance values of the light detectors at a nominal exposure time aredetermined. The maximum irradiance value of the light detectors(E_(Detector max)) captures the maximum light level which the lightdetectors can manage under the nominal exposure time. E_(Detector max)may be determined empirically or from the corresponding maximum radiancevalue of the array of light detectors (L_(Detector max)) associated withE_(Sun obs).

At step 1220, a range of reflectance of a target(s) during theacquisition campaign is characterized. The range includes thereflectance values between minimum reflectivity of the target(ρ_(Min target)) and the maximum reflectivity of the target(ρ_(Max target)). In some embodiments, the reflectance range, onlyρ_(Min target), or only ρ_(Max target) is used at step 1225. The rangeof reflectance values of the target may be obtained from publiclyavailable and/or proprietary radiative transfer models.

At step 1225, a dynamic link/transformation between readings of theincident light sensor and exposure time of the target looking sensor isestablished by implementing Equations 3 and 4 in the system controller.The proportion between the observed solar irradiance (E_(Sun obs)) andthe maximum target detector irradiance (E_(Detector max)) may be used toscale the nominal exposure time of the array light detectors in thetarget looking sensor. A factor, such as ρ_(Max target), may be used tonarrow the range of sensitivity for better sensitivity to signals of thetarget.

In some embodiments, characterizing critical energy levels for the lightdetector of the incident light sensor at step 1205, characterizingcritical energy levels of the array of light detectors of the targetlooking sensor at step 1215, and/or characterizing range of reflectanceof reflectance of the target(s) at step 1220 mean or otherwise relate tocapturing relationships between commercial grade sensor readings andprecise measurements of light by scientific instruments.

FIG. 13 illustrates an example workflow for a UAS-carried imaging systemduring a UAS mission, in accordance with some embodiments. Workflow 1300includes operations, functions, and/or actions as illustrated by blocks1305-1345. Although the blocks 1305-1345 are illustrated in order, theblocks may also be performed in parallel, and/or in a different orderthan described herein. The workflow 1300 may also include additional orfewer blocks, as needed or desired. For example, the blocks 1305-1345can be combined into fewer blocks, divided into additional blocks,and/or removed based upon a desired implementation. In some embodiments,the workflow 1300 occurs after an in-lab characterization of an incidentlight sensor and/or a target looking sensor.

At block 1305, a user programs the maximum solar irradiance(E_(Sun max)) for the UAS mission via an input device (for example, atablet) coupled with the communication interface of the main systemcontrol board. This parameter is stored in the main system controlboard. Since the minimum solar irradiance (E_(Sun min)) relates to thelowest elevation angle allowed by the light diffuser 820, in someembodiments, E_(Sun min) is preprogrammed and stored in the main systemcontrol board.

At optional block 1310, in dotted outline, the user programs the type oftarget selected for imaging. The main system control may store aplurality of different targets and corresponding ranges of reflectanceof the targets. A target range may include the reflectance betweenminimum reflectivity of the target (ρ_(Min target)) and the maximumreflectivity of the target (ρ_(Max target)).

At block 1315, the UAS-carried imaging system enters an imageryacquisition cycle. During the imagery acquisition cycle, the incidentlight sensor uses the stored maximum solar irradiance (E_(Sun max)) toautomatically set its exposure time.

At block 1320, a reading is made by the light detector of the incidentlight sensor and is sent to the main system control board, where one ormore routines convert the reading into an observed solar irradiance(E_(Sun obs)) and, in turn, determines the maximum detector irradiance(E_(Detector max)) of the target looking sensor.

At block 1325, the target looking sensor automatically usesE_(Detector max) to set its exposure time. In some embodiments, thetarget looking sensor adjusts the exposure time according to the rangeof reflectance of the selected target type to narrow detectorsensitivity particular to the target type.

At block 1330, a reading is made by the array of light detectors of thetarget looking sensor during the exposure time and the raw image(reading) is sent back to the main system control board.

At block 1335, one or more routines, derived from the incident lightsensor characterization, convert the raw image to a radiance image andthen to an irradiance image. The observed solar irradiance (E_(Sun obs))is used to derive a reflectance image.

At block 1340, the reflectance image is stored on the system storagedevice.

At block 1345, the UAS-carried imaging system enters a new imageryacquisition cycle, resuming at block 1320, until the UAS mission iscompleted or otherwise stopped.

Reflectance images stored on the system storage device may be retrievedafter the UAS mission or, in real-time, during the UAS mission by theuser's device (tablet), the agricultural intelligence computer system130 of FIG. 1, or other computing devices.

Although the disclosure discusses the UAS-carried imaging system beingmounted to a UAS, it can be mounted to a fixed wing craft or other fieldequipment.

The approaches disclosed herein provide the practical result ofoptimizing the exposure time of a target looking sensor of a UAS-carriedimaging system during a UAS mission. Data from an incident light sensoris processed in real-time to calibrate exposure time of the targetlooking sensor to avoid overexposure and underexposure. A dynamiclink/transformation between readings of the incident light sensor andexposure time of the target looking sensor enables the UAS-carriedimaging system to adapt to illumination condition(s) when readings aretaken by the target looking sensor by using readings of the incidentlight sensor. The disclosed approaches eliminate material costs, laborcosts, and post-processing calibration efforts that would otherwise berequired, for all the reasons set forth in the preceding paragraphs.

4. Other Aspects of Disclosure

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. Thus, the sole and exclusive indicatorof what is the invention and, is intended by the applicants to be theinvention, is the set of claims that issue from this application, in thespecific form in which such claims issue, including any subsequentcorrection. Any definitions expressly set forth herein for termscontained in such claims shall govern the meaning of such terms as usedin the claims. Hence, no limitation, element, property, feature,advantage or attribute that is not expressly recited in a claim shouldlimit the scope of such claim in any way. The specification and drawingsare, accordingly, to be regarded in an illustrative rather than arestrictive sense.

As used herein the terms “include” and “comprise” (and variations ofthose terms, such as “including”, “includes”, “comprising”, “comprises”,“comprised” and the like) are intended to be inclusive and are notintended to exclude further features, components, integers or steps.

Various operations have been described using flowcharts. In certaincases, the functionality/processing of a given flowchart step may beperformed in different ways to that described and/or by differentsystems or system modules. Furthermore, in some cases a given operationdepicted by a flowchart may be divided into multiple operations and/ormultiple flowchart operations may be combined into a single operation.Furthermore, in certain cases the order of operations as depicted in aflowchart and described may be able to be changed without departing fromthe scope of the present disclosure.

It will be understood that the embodiments disclosed and defined in thisspecification extends to all alternative combinations of two or more ofthe individual features mentioned or evident from the text or drawings.All of these different combinations constitute various alternativeaspects of the embodiments.

What is claimed is:
 1. A computer-implemented method of calibrating animaging system in real-time, comprising: under control of stored programinstructions executed using a processor, obtaining a first reading by afirst sensor; by executing the instructions, establishing a dynamic linkbetween the first reading and exposure time of a second sensor; byexecuting the instructions, using the dynamic link to control theexposure time of the second sensor; by executing the instructions,obtaining a second reading by the second sensor during the controlledexposure time; wherein the method is performed by one or more computingdevices.
 2. The computer-implemented method of claim 1, wherein theobtaining the first reading, the establishing, the using, and theobtaining the second reading are performed using a processor of anunmanned aircraft system (UAS) during one UAS mission.
 3. Thecomputer-implemented method of claim 2, wherein the obtaining the firstreading includes: obtaining solar irradiance (E_(Sun min)) for a givenwavelength at the lowest elevation angle allowed by a light diffuser ofthe first sensor; obtaining solar irradiance (E_(Sun max)) for the givenwavelength at the maximum elevation angle of the UAS mission;calibrating exposure time of the first sensor to capture readingsbetween E_(Sun min) and E_(Sun max); transforming the first reading intoan observed solar irradiance E_(Sun obs).
 4. The computer-implementedmethod of claim 3, wherein the establishing includes: determining, basedon E_(Sun obs), the maximum radiance value (L_(Detector max)) of anarray of light detectors of the second sensor; determining, based onL_(Detector max), the maximum irradiance value (E_(Detector max)) of anarray of light detectors of the second sensor.
 5. Thecomputer-implemented method of claim 4, wherein the dynamic link is aproportion between E_(Sun obs) and E_(Detector max).
 6. Thecomputer-implemented method of claim 5, wherein the using includesapplying the proportion as a scale to a nominal exposure time of thesecond sensor.
 7. The computer-implemented method of claim 5, whereinthe dynamic link includes a factor.
 8. The computer-implemented methodof claim 7, wherein the factor is maximum reflectivity of a target(ρ_(Max target)).
 9. The computer-implemented method of claim 1, whereinthe imaging system is coupled with an unmanned aircraft system (UAS).10. The computer-implemented method of claim 9, wherein the first sensoris an upwards looking sensor on the UAS and the second sensor is adownwards looking sensor on the UAS.
 11. An imaging system comprising: afirst sensor, a second sensor, and a system control board allcommunicatively coupled together; wherein the first sensor is configuredto obtain a first reading; wherein the system control board isconfigured to: establish a dynamic link between the first reading and anexposure time of the second sensor; use the dynamic link to control theexposure time of the second sensor; wherein the second sensor isconfigured to obtain a second reading during the controlled exposuretime.
 12. The imaging system of claim 11, wherein communication betweenthe first sensor, the second sensor, and the system control board isprovided in real-time during one unmanned aircraft system (UAS) mission.13. The imaging system of claim 12, wherein the first reading isobtained by: obtaining solar irradiance (E_(Sun min)) for a givenwavelength at the lowest elevation angle allowed by a light diffuser ofthe first sensor; obtaining solar irradiance (E_(Sun max)) for the givenwavelength at the maximum elevation angle of the UAS mission;calibrating exposure time of the first sensor to capture readingsbetween E_(Sun min) and E_(Sun max); transforming the first reading intoan observed solar irradiance E_(Sun obs).
 14. The imaging system ofclaim 13, wherein the dynamic link is established by: determining, basedon E_(Sun obs), the maximum radiance value (L_(Detector max)) of anarray of light detectors of the second sensor; determining, based onL_(Detector max), the maximum irradiance value (E_(Detector max)) of anarray of light detectors of the second sensor.
 15. The imaging system ofclaim 14, wherein the dynamic link is a proportion between E_(Sun obs)and E_(Detector max).
 16. The imaging system of claim 15, wherein theproportion is applied as a scale to a nominal exposure time of thesecond sensor.
 17. The imaging system of claim 15, wherein the dynamiclink includes a factor.
 18. The imaging system of claim 17, wherein thefactor is maximum reflectivity of a target (ρ_(Max target)).
 19. Theimaging system of claim 11, wherein the imaging system is coupled withan unmanned aircraft system (UAS).
 20. The imaging system of claim 19,wherein the first sensor is an upwards looking sensor on the UAS and thesecond sensor is a downwards looking sensor on the UAS.